ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY

PERSPECTIVE

Ying Zhou, Joseph Davis

School of Information Technologie, The University of Sydney, Australia

Keywords: Weblog, community, social network, social tie.

Abstract: In this paper, we report a two level study on weblog link structures. At the micro level, we carried out an in-

depth investigation of individual weblogs. Our goal was to obtain some preliminary understanding of the

different types of links that might indicate underlying communities of bloggers. Complete and detailed link

data was collected from eight weblogs followed by a variety of analyses. The result shows that both

incoming and outgoings follow Zipf like distribution in terms of the sources of those links. These suggest

clustering patterns (communities) within the whole blogspace. We also examine the temporal aspects of

weblogs. The average life span of a weblog entry is fairly long in most of our sample cases. In addition,

analysis on individual comment authors shows that in average, active comment authors maintain a rather

long relationship with a certain weblog. It provides evidence that historical data may be useful in

understanding weblog communities. On a larger scale, we developed a program to collect complete link

data from large number of interconnected weblogs and performed cluster analysis on it. Communities with

common topics are successfully extracted using those link data.

1 INTRODUCTION

Weblogs are web pages with several dated entries

usually arranged in reverse chronological order

(Kuma et al. 2004). This new form of online diary

has become an influential web application, with

thousands of blogs added on the web everyday. In

general, blog sites cover a wide range of topics. Sites

devoted to politics or technology-related topics

usually receive thousands of hits per day.

Blogs usually contain a large number of links to

other pages (Barabasi et al. 2001). This could be

links to regular webpages or links to other blog

entries. Each entry of a blog has its own “permalink”

(permanent link). It could be an individual webpage

or a section in a webpage. Blogs with similar topic

are usually interwoven into a network of

communities referred to as blogspace (Searls & Sifry

2003). Within a blogspace, bloggers list blogs they

read and other links on the sidebar, sometimes

referred to as blogrolls. They comment on each

other’s postings, generating periods of bursty

activities around interesting topics. Such bursty

activities are usually exemplified by heavy linkage

amongst the blogs involved with in a time interval

(Kumar et al. 2003). Blogs have evolved into both

link magnets and sources of links on Internet. The

result, as illustrated by Searls and Sifry (2003), is

both striking and to be expected, They state “name a

topic with a community of interest around it. Now

go to Google and look it up. There is a good chance

one or more of the top results will include

somebody’s weblog (aka blog)”. The example given

by them are: 802.11b, Segway and webblog. For

each search term, Google listed a weblog among the

top three results, which still holds at the time of

writing of this paper. There are also reports that

people prefer to have the latest news or development

trends through blogs rather than through traditional

media.

Blogspace represents a new form of online

community as well as a new form of online

knowledge repository connected by hyperlinks. Yet

it is not an easy task for a newcomer to discover the

virtual communities if they are interested in tracking

particular community discussions. Google recently

released a beta version of blog search services

(http://blogsearch.google.com/). It indexes a huge

collection of blog and news feeds. This search

service is based on the feed data. Most query results

point to individual entry of a weblog, with a few

highlighted matched entire weblogs appear on the

top. Beside the latest Google blog search, a few

specialized blog/news search engine has also

Zhou Y. and Davis J. (2006).

ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY PERSPECTIVE.

In Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Society, e-Business and

e-Government / e-Learning, pages 13-20

DOI: 10.5220/0001248200130020

 SciTePress

emerged, including bloglines.com, daypop.com and

blogdex.com. Each has its own copy of a collection

of weblogs and can perform content related search

for weblogs and news. Most of them are focused on

page-level search rather than community oriented

search. Hence the results are usually a mixture of

regular news pages and some blog pages based on

conventional static page rank algorithm. Weblogs,

compared the regular webpages are more dynamic

and evolves very quickly. Besies, People typically

search search weblogs for a different purpose. Most

of the time, they are trying to find a weblog that they

can keep reading regularly. A desirable weblog

search and ranking service requires different

methods to model and organize the whole blog

space. Understanding of individual blogs as well as

the structure of linkage interaction among weblogs is

important in the design of a weblog search service.

We carried out a two level study on weblog

search. We first conduct a case study on a number of

weblogs and investigated the feature of link

component within each of them. This case study

tries addresses the following questions: what kind of

data should we collect to study the community of

weblogs? what is the average life span of a weblog

entry? what are the general interaction patterns of a

weblog with other weblogs? Based on the findings

of the case study we conducted a larger scale

experiment to extract weblog communities by

exploiting their link structure.

2 RELATED WORKS

At a macro scale there are Gruhl et al (2004)’s

information diffusion study and Kumar et. Al

(2003)’s bursty evolution study. Both studies draw

on a large collection of weblogs over time. Gruhl

et.al (2004) utilizes the epidemic model of disease-

propagation to investigate the dynamics of

information propagation through networks of

bloggers. Their work can help to identify the hottest

topics and predict the diffusion of certain piece of

information in the community of bloggers. Kumar

et. al adopted the notion of time graph and use that

as a basis to extract temporal communities and

studied bursty behavior. They argued that blog

communities have striking temporal characteristis; in

particular, communities only formed when an

interesting topic arose and it faded away after a

certain period.

On a micro scale, Judit Bar-llan (2004)

monitored 15 sample weblogs for 2 months and

generated statistics related to the blogs and posts

such as, average postings per day, number of posting

day in a period of time, average links per post and so

on. The majority of her sample weblogs are

technology or research oriented. Her results show

that the topics of most postings in the blogs were

typically closely related to the declared topics. Since

the data collected by Bar-llan were over a two-

month period, it cannot give a complete picture of

long running blogs and their evolution.

3 BLOG COMMUNITIES

We define a blog community as a network of

weblogs with similar topics connected through

hyperlinks. Within the community, bloggers may

read, write and comment on other bloggers’ entries.

These are reflected by the three types of links

indicating certain interactions between two weblogs.

They are candidate links we need to consider for

discovering and constructing the blogspace.

Many bloggers list a few other blogs they read

on the siderbar. The list is called a blogroll. The

blogroll links indicate a read relationship between

this blogger and others.

It is very common for a blogger to cite or

reference another blogger’s writing on his/her own

post. This indicates a response relationship between

the two bloggers.

Bloggers may respond to one another’s writing

through other channels. They may leave a comment

on the posting site. Sometimes, a commenter

identifies self by a link to his or her own weblog in

the comment body. This enables a crawler to build

the connection between the two weblogs. In

addition, some blog authoring tools provide

“trackback” feature. This is a protocol that a

responder can use to notify the author that he(she)

has cited a particular article of the author in his(her)

own blog. The notification is achieved by sending a

ping message to the original blog entry, which will

then update its trackback list to include the senders’s

URL. Trackback links make it very easy to get all

incoming links of a particular blog entry. However,

this feature is not used extensively at present.

4 METHODOLOGY

In this study, we first take a micro scale approach

and closely investigated links in a few individual

weblogs and the three different types of associations

between blogs. The micro level is more appropriate

here since we are not going to study the general

structure of the whole blogspace. Rather, we are

more interested in the individual weblogs, and their

communication pattern with other weblogs.

WEBIST 2006 - SOCIETY, E-BUSINESS AND E-GOVERNMENT

The sample we traced consists of eight Weblogs.

The main criterion we use to choose the sample

cases is that the weblogs should have been running

actively for some time to make sure there are enough

data. In addition, the sample weblog should have

extensive comments to ensure the activeness of the

interactions. The cases chosen include two

cooperate weblogs, four pure technology weblogs

and two weblogs on personal opinions. These are:

1. MSN Search's Weblog (http:// blogs.msdn.

com/msnsearch/): an official weblog on MSN's

search related products and discussion since

November 2004.

2. Yahoo! search weblog (http://ysearchblog.

com): a weblog written by Yahoo! staff on

Yahoo! search product since August 2004.

3. Fabulous Adventures In Coding (http://

blogs.msdn.com/ericlippert/): a weblog written

by Eric Lippert discussing all sorts of coding

issues, .NET technology and a few other

things. It is started on September, 2003.

4. Sorting it all out (http://blogs.msdn.com/

michkap/): a weblog written by Michael Kaplan

mainly about locales, keyboards, Unicode and

other language related techniques. It is started

on November 2004.

5. Micro persuasion (

http://www.micropersuasion.

com/): a weblog written by Steve Rubel on “how

new technologies are transforming marketing,

media and public relations”. The weblog started

since since April 2004. There are many articles

regarding blogging news, practices and systems

in micropersuasion.

6. Schneier on Security

(http://www.schneier.

com/blog/) : Bruce Schneier’s weblog on

security and security technology since October,

2004.

7. BuzzMachine(http://www.buzzmachine.com/):

started since July 2005, buzzmachine is a

weblog written by Jeff Jarvis, currently the

president and creative director of Advance.net.

Jeff Jarvis is a high profile media people and

long time supporter of weblogs. Buzzmachine

has a wide coverage on many topics including

weblogs, newspapers, open source and politics.

8. Hot Points with BOB Parsons (http://

www.bobparsons.com/): a weblog written by

Bob Parsons, the president and founder of

GoDaddy.com, a company that provide Internet

domain name registration, web hosting, email

accounting and lots of other Internet related

services. The weblog contains his thoughts and

opinions on Internet innovations and lots of

other things. It is started on December 2004

All the chosen weblogs have different layouts. A

few specialized web crawlers were developed to

extract and collect the required information from

them. The customized crawlers can accurately

extract specified information, which is different

from the simple heuristic based crawling algorithm

used in Kumar et al. (2003) study. Information

collected include blogroll links, links appeared in

each entry, and comments made on each entry. Each

entry's publishing date and time as well each

comment's publishing date and time are also

collected. The links referenced in entries are placed

in two broad categories: blog link and other link.

Blog link points to another webblog or webblog

entry. We use the algorithm proposed by Ceglowski

(2003) to judge if a link point to a blog entry. The

data collection was carried out in October 2005. All

data were current up to that month.

5 RESULTS

5.1 Basic Demographic Information

Table 1 gives a summary of the basic information

based on the target weblogs. For all weblogs except

Schneier, we collected all data from the very first

posting. The number of occurrences for each type of

interactions is given to provide an overall picture of

the intensity of the communication coming in and

going out of a particular weblog. All of our target

weblogs have significant levels of communication

with other sites or weblogs. They referenced many

web pages and also attracted lots of discussions on

their own sites.

Table 1: Basic demographic information.

Blogroll

Link

No. of

Entries

Entry Link Comments

MSN Search 9 77 468

( 6)**

1545

(20)

YSearchblog 21 55 320

(5.9)

634

(11.7)

MichKap 0 887 4398

(4.9)

4272

(4.8)

EricLippert 12 605 889

(1.5)

4995

(8.3)

Micropersuasio

0 2548 7252

(2.8)

4672

(1.8)

Schneier* 0 86 200

(2.3)

3354

(39)

buzzmachine 1 580 4211

(7.3)

8485

(14.6)

BobParsons 0 68 197

(2.9)

5924

(87)

* Only 3 months data of this weblog is collected

** average number

ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY PERSPECTIVE

5.2 Tie Strength Distribution

The blog community can be viewed as a special type

of social network. A social network is a set of people

or groups connected with each other under a

particular relationship (Wasserman & Faust 1994).

Examples of typical social networks include the

friendship network of high school students, email

network of employees, and scientific co-authorship

network of academics (c.f. Barabasi et al. 2001,

Newman 2001). In social network terminology, the

people or groups are called "actors" and the

connections are called "ties". In a blog community

context, the bloggers who write weblogs are the

actors and the hyperlinks between weblogs are the

ties between two bloggers. If we consider each link

as a communication instance between two bloggers,

we can measure the strength of the tie according to

the number of links between two particular weblogs.

In this section, we will use links appearing in blog

entries and links created by comment authors to

study the distribution of tie strength for each

particular weblog.

Figure 1 shows the tie strength distribution for

the outgoing links found in weblog entry. The x-axis

represents the number of times a weblog has been

referenced (strength score of tie) and the y-axis

represents the number of weblogs that have been

references for a particular number of times (number

of ties with a certain strength score). For instance, in

Michkap weblog, we have 15 other weblogs being

referenced by Michkap once (15 weak ties), 1

weblogs being referenced by MichKap 10 times (1

strong tie with strength score 10) and so on.

Figure 1: Outgoing tie strength distribution.

All links appearing in the blog entry of blog type

are extracted. Those links usually point to a blog

entry of another weblog. To construct the tie

between two weblogs, we need to find weblog home

page URL of those links. This is achieved through

the RSS or ATOM feed of a weblog. The <link>

value of the <channel> element in RSS usually gives

the homepage of a weblog. By replacing the actual

links with its home page URL we can easily count

the number of links between two weblogs and

discover how many strong and weak ties exist for a

weblog.

Five weblogs are included in the diagram. The

rest do not have enough links pointing to other blog

entries. The chart on the bottom left panel contains

data for two weblogs. The data from all five weblogs

follows Zipf-like distribution. From the social

network point of view, the observation can be

interpreted as a weblog has many weak ties (being

referred once or twice), and only a few strong ties

(being referred many times) to other weblogs.

Similar procedures are performed on the

incoming ties reconstructed from the comment

section of each weblog entries. Each unique

comment author is considered as an agent in a social

network and one comment is considered as a

communication instance between the comment

author (usually a blogger) and the blogger. We use

the number of comments left by the same author to

measure the strength of the tie between comment

author and blogger. Figure 2 shows the tie strength

distribution for the incoming ties. The x-axis

represents the number of comments a reader left

(strength score of tie) and the y-axis represents the

number of readers that have left a particular number

of comments (number of ties with a certain strength

score). All weblogs show clear Zipf-like

distributions. All weblogs have large number of

occasional visitors who only made one or two

comments and a few frequent visitors who made lots

of comments.

The Zipf like distribution of the tie strength is

consistent with Granovetter (1983) view of social

world. In his description, social world is structured

with highly connected clusters (strong ties) with

many external weak tie connecting these clusters.

The observation implies the existence of clusters

consisting of weblogs with strong ties to each other

in the blogspace. These clusters are actually weblog

communities of interest. It also indicates that we can

discover these blog communities by measuring the

link strength between weblogs.

WEBIST 2006 - SOCIETY, E-BUSINESS AND E-GOVERNMENT

5.3 Blog Entry Life Span

The study of weblog community would involve both

spatial and temporal dimensions. In the previous

section, we investigate the dynamics of weblog

interactions and discovered that around each weblog,

there is a small circle with highly frequent

interaction. These would form the basis for closely

knit weblog communities. Yet the questions

regarding how stable these small circles might be

along a weblog's life span and how frequently those

communities might evolve remain unanswered.

Weblogs, nicknamed as online diary, share many

features of news sites. They both focus on current

events, be it a social, political event or a technical

problem recently raised. They are both updated

frequently and have some established readership

base. This raises the problem of the value of back

issues. If we are going to study the community

around a particular topic, how much historical data

do we need to collect to ensure the accuracy of our

community information.

To develop a better understanding of the

problem, we used the publishing date and time

information recorded on each blog entry and

comment to get some preliminary idea of the

average life span of a blog entry. We used the latest

time a comment was made by somebody other than

the author and the blog posting time to estimate the

average life span of a blog entry. The life spans of

entries vary significantly with most posts having a

one day life span and a few having life spans as high

as nearly two years. For instance, one entry in Eric

Lipert’s blog (http://blogs.msdn.com/ericlippert/

rchive/2003/10/06/53150.aspx ) originally posted in

November, 2003 achieved some recent discussion

around two years later. We discovered quite a few

posts in Eric’s weblog with more than 600 days’ of

life span. Table 2 give the descriptive statistics of

entry life span for each weblog in the sample.

Although varying enormousely in terms of average

life span, a consistent message coming through all

those cases is that historical entries are not

completely ignored by blog readers. This suggests

that in analyzing weblog communities, we do have

to include some historical data.

Table 2: Weblog entry life span.

Unit: day Mean S.D Range

MSN Search 31.4 50.1 210 (330)*

YSearchblog 60.3 86.5 343 (420)

MichKap 25.7 54.1 282 (330)

EricLippert 75.0 168.9 704 (760)

Micropersuasion 6.0 36.7 482 (550)

Schneier 7.8 9.3 47

buzzmachine 7.2 13.3 87 (110)

BobParsons 84.9 71.6 295(300)

* Weblog running days to October, 2005

5.4 Temporal Aspects

The concept of time graph and temporal community

introduced in (Kumar et al. 2003) suggests certain

bursty patterns along a weblog’s life time. These

may be generated by totally different groups of

people or the same group of people. On the one

hand, if bursts are generated by different groups, a

natural conclusion would be that a weblog may be in

Figure 2: Incoming tie strength distribution.

ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY PERSPECTIVE

different communities from time to time. In that

case, it would be difficult to measure the stability of

a community and predict any future interaction

among different weblogs. On the other hand, if

most bursts are generated by relatively stable groups

of members, this suggests that communities around a

weblogs are fairly stable and it is reasonable to use

historical data to predict future trends.

We took a few weblogs in our sample with

relatively long life time and extract the most active

five comment authors along with all instances of

their communications. The purpose was to see if

there was clustering pattern along the time line, that

is, comment authors tend to comment with in a short

duration and may never come back. Figure 3 shows

the monthly communication intensity distribution of

the top five comment authors in MichKap,

EricLippert and Micropersuasion weblogs. The size

of the area is proportional to the number of

communications in that month. We do see peaks

from time to time which support findings in (Kumar

et al. 2003). In Figure 3, different colors represent

different authors. The numbers in the legend indicate

the total number of communications made by that

author. A consistent pattern is that majority of

authors interact with the target weblog for a

relatively long period of time. Except for a few

cases, many authors maintain the commenting

relationship for more than one year. However, some

may be more active in the early stage, for instance,

Dan Shappire in EricLippert Weblog. Others may be

more active in late stage, for instance, Nicolas Allan

in EricLippert Weblog. Both covered a period of

more than one-and-a-half year. This suggests that

the communities of readership for most weblogs in

our sample are quite stable for a certain period of

time.

6 MACRO-SCALE APPLICATION

The above case study indicates the existence of

community of webloggers and the types of link data

that need to be collected to discover the weblog

communities. To test those findings on a much

larger scale, a web crawler was developed to collect

link data from large collection of weblogs.

Clustering is then applied on the data to identify the

community structure.

The weblog crawler takes a weblog URL as seed

and incrementally adds linked pages in the

collection. Table 1 illustrates the main crawling

algorithm. This weblog crawler can extract complete

set of links from a weblog. This includes links on

sidebar, links in blog entry and links in comment

section. If any of those links points to another

weblog, a complete link set from that weblog will be

extracted as well. The crawling depth is controlled

by the maxDepth variable and is currently set to 6 to

reflect the “six degrees of separation” rule (Barabasi

2002).

Internet was reported to have 19 degree of

separation between any individual webpages

(Barabasi 2002). Since our unit of analysis is the

weblog and not the individual page, and each

MichKap

100

150

200

250

v-04

n-05

Mar-0

May

Jul-05

M aurit s (221): ht t p :/ / www.g eo cit ies.co m/ mvaneerde/

Dean Harding(109):http://www.codeka.com/blogs/

Mihai(93)

Norman Diamond (49)

Shaun B ed ingf ield ( 4 8 )

EricLippert

Aug-03

eb-04

ov-04

Feb-05

-0

Mike(48):http://w w w .mikepope.com/blog

Peter Torr(108):http://blogs.gotsotnet.com/pto

Norman Diamond(53)

Dan Shappir(193)

Nic holas A llen

(

146

)

Micropersuasion

Apr-04

Jun-04

Aug-04

Oct-04

Dec-04

Feb-05

Apr-05

Jun-05

Aug-05

Oct-05

Robert Scoble(45):http://scoble.w eblogs.com

david parmet(50):http://w w w .parmet.net/pr/

Tom(70):http://w w w .themediadrop.com/

Randy Charles Morin(117)

Jeremy Pepper (135):http://pop-pr.blogspot.co

Figure 3: Communication Strength Distribution.

WEBIST 2006 - SOCIETY, E-BUSINESS AND E-GOVERNMENT

weblog represents a person who writes it, we think it

is more appropriate to follow the six degrees of

separation concept in our research.

The crawling result is a collection of relational

records with two fields, the source weblog url and

the target weblog url. Each record indicates a link

from source to target, with a value of 1.

Table 3: Web crawler algorithm.

1 Url = seed url;

2 depth = 0;

3 method crawl (Url, depth)

4 if (depth < maxDepth)

5 for all hyper links link in Url

6 if link belongs to the same weblog

7 crawl (link, depth)

8 else if link is a weblog post

9 find the home page of link as link.home

10 add a record Url.home and link.home

11 crawl(link.home, depth + 1)

12 end if

13 end for

14 end if

15 end method

Figure 4: Web Services blog communities.

We take Savas Parastadist’s weblog

(Savas.parastatidist.name) as seed in the experiment

to run the web crawler. The key theme of Savas

Parastadist’s weblog is web services standards and

products. The result contains around 3800 unique

weblogs and over 33000 links. Multiple links

between two weblogs are removed by summing up

the number of links and use it as the link value

between two weblogs. We then use Pajek

(http://vlado.fmf.uni-lj.si/pub/networks/pajek/) to

perform clustering on the collection. The clustering

algorithm tries to discover clusters within a large

graph so that each node in a cluster should have

more communication with other nodes inside the

cluster than with nodes outside the cluster.

Figure 4 gives a visual display of the

communities extracted from the data and the

structure within each cluster. In total, 15

communities are identified from the data.

We inspected members in those communities.

Table 4 give a list of members in the community

that contains the original seed. The topics of those

member weblogs include .NET, XML and general

web services. Some communities are more focused

on general web services while others focused on

certain web services-related technologies such as

XML and .NET. The result shows that it is possible

to identify communities based on complete link data

of weblogs.

However, we can collect more recent data to

build communities based on clustering technique as

well. From the result on individual comment author,

not all comment authors are active throughout the

life span of a weblog. Some may be more active in

the early stage, while others may be more active in

late stages. The clustering algorithm can be applied

on feed data rather than on entire weblog pages (as

what Google BlogSearch does). A sliding time

window can be used to update the blogspace with

new feeds and to remove obsolete data. However,

we believe that there will not be a standard window

size that can fit all sorts of welbogs. Different

weblogs usually have very different average life

spans.

7 CONCLUSIONS

This research examined eight weblogs as special

cases to study the link structure within weblogs.

Two different types of links were examined: links

embedded on blog entry and links created by

comment authors. The result shows that most

weblogs cited a wide range of other weblogs with

approximate Zipf-like distributions. Many weblogs

have large number of readers commenting on their

writings. The distribution of commenting

communication intensity also follows Zipf like

curve. Majority of the readers left one or two

comments on the average with a small number of

readers left large number of comments. These Zipf

like distributions observed with respect to entry links

and comment authors suggest clustering patterns

(communities) within the whole blogspace. We also

examine the temporal features of weblogs. The

average life span of a weblog entry is fairly long in

most our sample cases. In addition, analysis of

ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY PERSPECTIVE

individual comment authors shows that in average,

some active comment authors maintain a rather long

relationship with a certain weblog. This suggests

that historical data may be useful in understanding

weblog communities. To test the above findings, we

developed a program to collect complete link data

from large number of interconnected weblogs and

performed clustering analysis on it. Communities

with common topics are successfully extracted from

these link data.

Table 4: Community member list.

1 http://savas.parastatidis.name

2 http://research.microsoft.com/news/msrnews/

3 http://pluralsight.com/blogs/dbox/

4 http://pluralsight.com/blogs/craig/

5 http://pluralsight.com/blogs/tewald/

6 http://pluralsight.com/blogs/aaron/ ***

7 http://pluralsight.com/blogs/keith/

8 http://pluralsight.com/blogs/fritz/

9 http://pluralsight.com/blogs/mgudgin/

10 http://msdn.microsoft.com/msdnmag/

11 http://unboxedsolutions.com/sean/

12 http://glazkov.com/blog/

13 http://devauthority.com/blogs/csteen *

14 http://weblogs.asp.net/ericjsmith/

15 http://blogs.msdn.com/smguest

16 http://samgentile.com/blog/

17 http://weblogs.asp.net/rhurlbut/

18 http://jcooney.net/

19 http://blogs.msdn.com/yassers

20 http://weblogs.asp.net/cweyer/

21 http://www.innoq.com/blog/st/

22 http://www.jonfancey.com/

23 http://codebetter.com/blogs/jeffrey.palermo

24 http://blog.whatfettle.com/

25 http://blogs.msdn.com/brada

26 http://blogs.msdn.com/mpowell

27 http://weblogs.asp.net/jgaylord/

28 http://blogs.msdn.com/robcaron **

29 http://weblogs.asp.net/despos/

30 http://www.theserverside.net

31 http://blogs.msdn.com/mfussell

32 http://weblogs.asp.net/mnissen/

33 http://blogs.msdn.com/trobbins

34 http://blogs.msdn.com/tomholl

35 http://weblogs.asp.net/wallym/

36 http://dotnetjunkies.com/WebLog/barblog

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